Key takeaways
- Apollo finds limited AI payoff outside technology giants
- Revenue per worker and profit margins remain flat
- Businesses increasingly question returns on AI spending
Corporate America's artificial intelligence boom is producing a widening financial divide, with the largest technology companies recording stronger revenue and profitability while most other major U.S. businesses have yet to demonstrate comparable gains.
In an October 24 research note, Apollo Global Management chief economist Torsten Slok found that revenue per worker and profit margins have increased among the Magnificent Seven but remained broadly flat across the other 493 companies in the S&P 500.
The findings raise an uncomfortable question for companies investing in AI tools: when will the promised efficiency improvements actually reach their financial statements?
Apollo Finds AI Benefits Concentrated Among Technology Giants
Apollo's analysis compares the financial performance of the Magnificent Seven with the remaining S&P 500 companies.
The first chart shows inflation-adjusted revenue per employee rising among the technology giants while remaining relatively unchanged across the broader group.
A second comparison identifies a similar divide in profit margins, with substantial improvement among the seven largest technology companies but little aggregate progress elsewhere.
Apollo also examined total factor productivity, a measure of economic efficiency that accounts for labor and capital inputs, and found no clear acceleration attributable to AI.
The pattern reinforces earlier concerns about AI investment returns, particularly as companies devote growing budgets to software subscriptions, automation and computing infrastructure.
However, the findings do not prove AI has created no value for individual businesses. Aggregate financial data can conceal productivity improvements offset by wages, financing costs or other expenses.
Why Companies Are Reconsidering AI Spending
Apollo argues that AI spending remains concentrated among a relatively small number of businesses, while mainstream corporations are becoming more selective about purchasing increasingly sophisticated models.
Slok also points to differences between venture-backed companies and established businesses, with startups increasing spending on AI tokens while larger organizations question whether premium models justify their costs.
That distinction was visible in recent enterprise spending research, which showed slowing expenditure growth even as AI adoption continued.
A September analysis cited by Forbes found that 69% of S&P 500 companies were actively deploying AI, but only 29% reported quantified outcomes and just 2% tracked a performance metric over time.
These figures suggest that widespread adoption does not necessarily translate into measurable financial returns.
Meanwhile, AI infrastructure investment continues expanding. Amazon's capital spending plans illustrate how companies can experience accelerating cloud demand while spending heavily enough to pressure free cash flow.
What Happens if AI Profits Remain Concentrated?
The financial consequences extend beyond businesses purchasing AI software.
Semiconductor manufacturers, cloud providers and data-center suppliers can generate revenue from infrastructure spending before their customers demonstrate profitable applications.
Coinpaper previously examined where AI spending goes, including payments to chipmakers, utilities and construction companies.
However, the sustainability of that spending ultimately depends on customers generating sufficient economic value.
Recent semiconductor stock losses demonstrate how quickly investors can question the industry's financial assumptions.
The concentration also raises broader financial-stability concerns when expensive projects rely on borrowing and expectations of substantial future earnings.
